Motion status identification is one of important content of context awareness. With popularization of mobile terminals, intelligent services with better experience may be provided for a user by inferring, using a mobile terminal, a motion status of the user, triggering, according to the motion status of the user, a change in a status or a function of the mobile terminal, and providing for the user an intelligent mobile application, such as personalized recommendation or accurate advertisement push according to an inferred higher-class user situation. In a mobile terminal application scenario, difficulties and challenges of motion status identification are how prepared identification is performed on motion of different users in a complex and changeable environment using sensors built in most mobile terminals and without the help of a function of another additional hardware. In consideration of limited computing, storage and energy resources of a terminal, an algorithm involved in motion status identification is as simple as possible, and a dependent function module cannot generate a large number of extra function overheads.
A user is positioned by means of satellite positioning (for example, a global positioning system (GPS)), and a difference between longitudes and a difference between latitudes within a time interval are converted into a motion speed for indirectly inferring a motion status of the user. This manner requires a satellite positioning function be additionally enabled. Generally, power consumption of a GPS on a mobile terminal is higher; time to first fix is longer; and this manner often cannot be used in a shaded area (for example, indoors or in an underground passage).
A motion status of a user is inferred by means of feature extraction, classification, and other means and according to data collected by an inertial sensor, such as an acceleration sensor or a gyroscope. Accuracy of identification using this method is more greatly affected by a placement position of a mobile phone and is more closely related to a posture and a motion habit of the user; algorithm complexity is generally higher; and a large number of training samples need to be collected in advance.
Standard deviation analysis is performed on wireless signal strength data of a base station on a traditional wireless cellular network, still and motion states are distinguished according to a standard deviation, and then, feature matching is performed on a signal sequence and a waveform in a sample library to distinguish two motion types: “walking” and “driving”. When the two motion types “walking” and “driving” are distinguished, similarity analysis needs to be performed on the waveform in real time. On one hand, calculation complexity is higher, and a challenge is brought to a processing capacity of a mobile terminal. On the other hand, signal samples need to be collected and stored in advance, and when there is a large amount of data in the sample library, a burden is brought to limited storage space of the mobile terminal.